The reciprocal associations between catastrophizing and pain outcomes in patients being treated for neuropathic pain: a cross-lagged panel analysis study
Bibliographic record
Abstract
Catastrophizing is recognized as a key psychosocial factor associated with pain-related negative outcomes in individuals with chronic pain. Longitudinal studies are needed to better understand the temporal relationship between these constructs. The aim of this study was to determine if changes in catastrophizing early in treatment predicted subsequent changes in pain intensity and interference later in treatment, or alternately, if early changes in pain intensity and interference predicted subsequent changes in catastrophizing. A total of 538 patients with neuropathic pain were recruited from 6 multidisciplinary pain clinics across Canada. Study participants were asked to complete measures of catastrophizing, pain intensity, and interference when first seen in the clinic and then again at 3- and 6-month follow-ups. Cross-lagged panel analyses were used to determine the temporal associations among the study variables. The results showed that decreases in catastrophizing early in treatment prospectively predicted improvement in both pain intensity and interference later in treatment. Converse temporal relationships were also found, where a reduction in pain intensity and interference early in treatment predicted a subsequent diminishing of catastrophizing. All 4 unique cross-lagged correlations significantly accounted for an additional 4% to 7% of the total variance. The findings are consistent with theoretical models hypothesizing a causal impact of catastrophizing on pain, suggesting a mutual causation between these factors. The results support that treatments targeting catastrophizing may influence other pain-related outcomes, and conversely that treatments aiming to reduce pain could potentially influence catastrophizing. There may therefore be multiple paths to positive outcomes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.032 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".